Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

Wang, Liang-Hung; Ding, Lin-Juan; Xie, Chao-Xin; Jiang, Su-Ya; Kuo, I‐Chun; Wang, Xinkang; Gao, Jie; Huang, Pao-Cheng; Abu, Patricia Angela R.

doi:10.1109/access.2021.3128736

Cited by 27 publications

(15 citation statements)

References 41 publications

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“…The performance metrics and error values are calculated in the model and discussed in this section. [11] 99.96 98.9 99.2 OTSU -Gamma [12] 98.25 95.47 97.72 Deep learning based PVC [13] N/A 99.2 N/A KNN-deep metric [14] 99.59 96.7 96.7 Ensemble classifier [15] 98 Error value is calculated for various numbers of epochs in the SP-MRF-BiLSTM for classification of PVC, as shown in Fig. 4.…”

Section: Resultsmentioning

confidence: 99%

“…The KNN deep metric [14] model has a second higher performance due to CNN based feature extraction. The OTSU-Gamma [12] and Hybrid feature extraction model [11] has considerable performance in PVC classification. The existing methods [11,12,14] have lower sensitivity and sensitivity is an important measure in the medical system.…”

Section: Resultsmentioning

confidence: 99%

“…The OTSU-Gamma [12] and Hybrid feature extraction model [11] has considerable performance in PVC classification. The existing methods [11,12,14] have lower sensitivity and sensitivity is an important measure in the medical system. The SP-MRF-BiLSTM model selects unique features to improve the efficiency and sensitivity of the model due to the spiral search process.…”

Section: Resultsmentioning

confidence: 99%

“…Wang [12] Sarshar [13] applied deep learning based PVC recognition model and evaluate the model using the MIT-BIH dataset. Three morphological features in statistical features such as QRS width, QR amplitude, RS amplitude, and 10 heartbeats.…”

Section: Literature Reviewmentioning

confidence: 99%

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Premature Ventricular Contraction Classification Based on Spiral Search - Manta Ray Foraging and Bi-LSTM

2022

IJIES

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Cardiovascular Diseases (CVDs) have become a burden to the healthcare system due to the increase in the number of patients and the ratio of mortality. Various techniques are applied for the classification of Premature Ventricular Contraction (PVC) and existing methods have lower sensitivity due to more similarity of features between classes. This research proposes the Spiral Search -Manta Ray Foraging (SP-MRF) algorithm to select the unique features that help to increase the sensitivity of the model. The Convolutional Neural Network (CNN) based model is used for the feature extraction from the input signals and the SP-MRF method selects features from extracted features. The Spiral Search technique increases the exploitation of the search method that helps to distinguish between more similar features and select unique features for classes. The selection of unique features helps to increase the sensitivity and efficiency of the model in PVC classification. The selected features were applied for the Bi-directional Long Short Term Memory (BiLSTM) for PVC classification and evaluated the performance. The efficiency of the SP-MRF-BiLSTM model is evaluated on the MIT-BIH dataset for PVC classification. The SP-MFR-BiLSTM model has 99.97 % accuracy, and 99.52 % sensitivity and the KNN-deep metric has 99.59 % accuracy and 96.7 % sensitivity in the model.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Premature Ventricular Contraction Classification Based on Spiral Search - Manta Ray Foraging and Bi-LSTM

2022

IJIES

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“…Mazidi et al [32] designed a linear kernel-based SVM classifier with morphology, time domain, time-frequency domain and nonlinear features for PVC recognition, the method achieved a higher overall ACC and Se (99.78% and 99.91%, respectively) than our method. Wang et al [34] proposed PVC detection scheme based on image processing and CNN for scanned clinical ECG reports, and their Se and ACC could reach 95.47% and 98.25%, respectively. However, our method was unsupervised while the training set used in their method was overlapped in their test set.…”

Section: Discussionmentioning

confidence: 99%

Robust PVC Identification by Fusing Expert System and Deep Learning

Cai¹,

Wang²,

Shen³

et al. 2022

Preprint

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Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion even early warning for physicians, however, they are mutually-exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. Long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the 3rd China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

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VedgeSat: An automated, open‐source toolkit for coastal change monitoring using satellite‐derived vegetation edges

Muir,

Hurst,

Richardson‐Foulger

et al. 2024

Earth Surf Processes Landf

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Public satellite platforms offer regular observations for global coastal monitoring and climate change risk management strategies. Unfortunately, shoreline positions derived from satellite imagery, representing changes in intertidal topography, are noisy and subject to tidal bias that requires correction. The seaward‐most vegetation boundary reflects a change indicator which shifts on event–decadal timescales, and informs coastal practitioners of storm damage, sediment availability and coastal landform health. We present and validate a new open‐source tool VedgeSat for identifying vegetation edges (VEs) from high (3 m) and moderate (10–30 m) resolution satellite imagery. The methodology is based on the CoastSat toolkit, with streamlined image processing using cloud‐based data management via Google Earth Engine. Images are classified using a newly trained vegetation‐specific neural network, and VEs are extracted at subpixel level using dynamic Weighted Peaks thresholding. We performed validation against ground surveys and manual digitisation of aerial imagery across eroding and accreting open coasts and estuarine environments at a site in Scotland. Smaller‐than‐pixel vegetation boundary detection was achieved across 83% of Sentinel‐2 imagery (Root Mean Square Error of 9.3 m). An overall RMSE of 19.0 m was achieved across Landsat 5 & 8, Sentinel‐2 and PlanetScope images. Performance varied by coastal geomorphology, with highest accuracies across sandy open coasts owing to high spectral contrast and less false positives from intertidal vegetation. The VedgeSat tool can be readily applied in tandem with waterlines near‐globally, to support adaptation decisions with historic coastal trends across the whole shoreface, even in normally data‐scarce areas.

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Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

Cited by 27 publications

References 41 publications

Premature Ventricular Contraction Classification Based on Spiral Search - Manta Ray Foraging and Bi-LSTM

Premature Ventricular Contraction Classification Based on Spiral Search - Manta Ray Foraging and Bi-LSTM

Robust PVC Identification by Fusing Expert System and Deep Learning

VedgeSat: An automated, open‐source toolkit for coastal change monitoring using satellite‐derived vegetation edges

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